OpenRaiser/Intern-Atlas huggingface dataset:
Dynamic Load Balancing Strategies for Graph Applications on GPUs
Venue: arXiv cs.DC 2017
Year: 2017

Acceleration of graph applications on GPUs has found large interest due to the ubiquitous use of graph processing in various domains. The inherent \textit{irregularity} in graph applications leads to several challenges for parallelization. A key challenge, which we address in this paper, is that of load-imbalance. If the work-assignment to threads uses node-based graph partitioning, it can result in skewed task-distribution, leading to poor load-balance. In contrast, if the work-assignment uses edge-based graph partitioning, the load-balancing is better, but the memory requirement is relatively higher. This makes it unsuitable for large graphs. In this work, we propose three techniques for improved load-balancing of graph applications on GPUs. Each technique brings in unique advantages, and a user may have to employ a specific technique based on the requirement. Using Breadth First Search and Single Source Shortest Paths as our processing kernels, we illustrate the effectiveness of each of the proposed techniques in comparison to the existing node-based and edge-based mechanisms.
Bluetooth 5: a concrete step forward towards the IoT
Venue: arXiv cs.NI 2017
Year: 2017

Six years after the adoption of the standard 4.0, the Bluetooth Special Interest Group (SIG), a non-profit association that deals with the study and the development of technology standards including those of Bluetooth, has officially released the main features of Bluetooth 5.0. It is one of the significant developments in short-range wireless communication technology. As stated by the SIG, the new standard will forever change the way people approach the Internet of Things (IoT), turning it into something that takes place around them in an almost natural and transparent way. In this article, the future IoT scenarios and use cases that justify the push for Bluetooth 5 are introduced. A set of new technical features that are included in Bluetooth 5 are presented, and their advantages and drawbacks are described.
Towards Automatic Generation of Entertaining Dialogues in Chinese Crosstalks
Venue: arXiv cs.CL 2017
Year: 2017

Crosstalk, also known by its Chinese name xiangsheng, is a traditional Chinese comedic performing art featuring jokes and funny dialogues, and one of China's most popular cultural elements. It is typically in the form of a dialogue between two performers for the purpose of bringing laughter to the audience, with one person acting as the leading comedian and the other as the supporting role. Though general dialogue generation has been widely explored in previous studies, it is unknown whether such entertaining dialogues can be automatically generated or not. In this paper, we for the first time investigate the possibility of automatic generation of entertaining dialogues in Chinese crosstalks. Given the utterance of the leading comedian in each dialogue, our task aims to generate the replying utterance of the supporting role. We propose a humor-enhanced translation model to address this task and human evaluation results demonstrate the efficacy of our proposed model. The feasibility of automatic entertaining dialogue generation is also verified.
On Spectral Analysis of the Internet Delay Space and Detecting Anomalous Routing Paths
Venue: arXiv cs.NI 2017
Year: 2017

Latency is one of the most critical performance metrics for a wide range of applications. Therefore, it is important to understand the underlying mechanisms that give rise to the observed latency values and diagnose the ones that are unexpectedly high. In this paper, we study the Internet delay space via robust principal component analysis (RPCA). Using RPCA, we show that the delay space, i.e. the matrix of measured round trip times between end hosts, can be decomposed into two components - the expected latency between end hosts with respect to the current state of the Internet and the inflation on the paths between the end hosts. Using this decomposition, first we study the well-known low-dimensionality phenomena of the delay space and ask what properties of the end hosts define the dimensions. Second, using the decomposition, we develop a filtering method to detect the paths which experience unexpected latencies and identify routing anomalies. We show that our filter successfully identifies an anomalous route even when its observed latency is not obviously high in magnitude.
Building Data-driven Models with Microstructural Images: Generalization and Interpretability
Venue: arXiv cs.AI 2017
Year: 2017

As data-driven methods rise in popularity in materials science applications, a key question is how these machine learning models can be used to understand microstructure. Given the importance of process-structure-property relations throughout materials science, it seems logical that models that can leverage microstructural data would be more capable of predicting property information. While there have been some recent attempts to use convolutional neural networks to understand microstructural images, these early studies have focused only on which featurizations yield the highest machine learning model accuracy for a single data set. This paper explores the use of convolutional neural networks for classifying microstructure with a more holistic set of objectives in mind: generalization between data sets, number of features required, and interpretability.
Early prediction of the duration of protests using probabilistic Latent Dirichlet Allocation and Decision Trees
Venue: arXiv cs.SI 2017
Year: 2017

Protests and agitations are an integral part of every democratic civil society. In recent years, South Africa has seen a large increase in its protests. The objective of this paper is to provide an early prediction of the duration of protests from its free flowing English text description. Free flowing descriptions of the protests help us in capturing its various nuances such as multiple causes, courses of actions etc. Next we use a combination of unsupervised learning (topic modeling) and supervised learning (decision trees) to predict the duration of the protests. Our results show a high degree (close to 90%) of accuracy in early prediction of the duration of protests.We expect the work to help police and other security services in planning and managing their resources in better handling protests in future.
Internet of Cloud: Security and Privacy issues
Venue: arXiv cs.CR 2017
Year: 2017

The synergy between the cloud and the IoT has emerged largely due to the cloud having attributes which directly benefit the IoT and enable its continued growth. IoT adopting Cloud services has brought new security challenges. In this book chapter, we pursue two main goals: 1) to analyse the different components of Cloud computing and the IoT and 2) to present security and privacy problems that these systems face. We thoroughly investigate current security and privacy preservation solutions that exist in this area, with an eye on the Industrial Internet of Things, discuss open issues and propose future directions
Autonomous Electric Race Car Design
Venue: arXiv cs.RO 2017
Year: 2017

Autonomous driving and electric vehicles are nowadays very active research and development areas. In this paper we present the conversion of a standard Kyburz eRod into an autonomous vehicle that can be operated in challenging environments such as Swiss mountain passes. The overall hardware and software architectures are described in detail with a special emphasis on the sensor requirements for autonomous vehicles operating in partially structured environments. Furthermore, the design process itself and the finalized system architecture are presented. The work shows state of the art results in localization and controls for self-driving high-performance electric vehicles. Test results of the overall system are presented, which show the importance of generalizable state estimation algorithms to handle a plethora of conditions.
This robot stinks! Differences between perceived mistreatment of robot and computer partners
Venue: arXiv cs.RO 2017
Year: 2017

Robots (and computers) are increasingly being used in scenarios where they interact socially with people. How people react to these agents is telling about the perceived animacy of such agents. Mistreatment of robots (or computers) by co-workers might provoke such telling reactions. The purpose of this study was to discover if people perceived mistreatment directed towards a robot any differently than toward a computer. This will provide some understanding of how people perceive robots in collaborative social settings. We conducted a between-subjects study with 80 participants. Participants worked cooperatively with either a robot or a computer which acted as the "recorder" for the group. A confederate either acted aggressively or neutrally towards the "recorder." We hypothesized that people would not socially accept mistreatment towards an agent that they felt was intelligent and similar to themselves; that participants would perceive the robot as more similar in appearance and emotional capability to themselves than a computer; and would observe more mistreatment. The final results supported our hypothesis; the participants observed mistreatment in the robot, but not the computer. Participants felt significantly more sympathetic towards the robot and also believed that it was much more emotionally capable.
How Polarized Have We Become? A Multimodal Classification of Trump Followers and Clinton Followers
Venue: arXiv cs.SI 2017
Year: 2017

Polarization in American politics has been extensively documented and analyzed for decades, and the phenomenon became all the more apparent during the 2016 presidential election, where Trump and Clinton depicted two radically different pictures of America. Inspired by this gaping polarization and the extensive utilization of Twitter during the 2016 presidential campaign, in this paper we take the first step in measuring polarization in social media and we attempt to predict individuals' Twitter following behavior through analyzing ones' everyday tweets, profile images and posted pictures. As such, we treat polarization as a classification problem and study to what extent Trump followers and Clinton followers on Twitter can be distinguished, which in turn serves as a metric of polarization in general. We apply LSTM to processing tweet features and we extract visual features using the VGG neural network. Integrating these two sets of features boosts the overall performance. We are able to achieve an accuracy of 69%, suggesting that the high degree of polarization recorded in the literature has started to manifest itself in social media as well.
Concave losses for robust dictionary learning
Venue: arXiv cs.LG 2017
Year: 2017

Traditional dictionary learning methods are based on quadratic convex loss function and thus are sensitive to outliers. In this paper, we propose a generic framework for robust dictionary learning based on concave losses. We provide results on composition of concave functions, notably regarding super-gradient computations, that are key for developing generic dictionary learning algorithms applicable to smooth and non-smooth losses. In order to improve identification of outliers, we introduce an initialization heuristic based on undercomplete dictionary learning. Experimental results using synthetic and real data demonstrate that our method is able to better detect outliers, is capable of generating better dictionaries, outperforming state-of-the-art methods such as K-SVD and LC-KSVD.
Statistical evaluation of visual quality metrics for image denoising
Venue: arXiv cs.CV 2017
Year: 2017

This paper studies the problem of full reference visual quality assessment of denoised images with a special emphasis on images with low contrast and noise-like texture. Denoising of such images together with noise removal often results in image details loss or smoothing. A new test image database, FLT, containing 75 noise-free "reference" images and 300 filtered ("distorted") images is developed. Each reference image, corrupted by an additive white Gaussian noise, is denoised by the BM3D filter with four different values of threshold parameter (four levels of noise suppression). After carrying out a perceptual quality assessment of distorted images, the mean opinion scores (MOS) are obtained and compared with the values of known full reference quality metrics. As a result, the Spearman Rank Order Correlation Coefficient (SROCC) between PSNR values and MOS has a value close to zero, and SROCC between values of known full-reference image visual quality metrics and MOS does not exceed 0.82 (which is reached by a new visual quality metric proposed in this paper). The FLT dataset is more complex than earlier datasets used for assessment of visual quality for image denoising. Thus, it can be effectively used to design new image visual quality metrics for image denoising.
Flight Trajectory Planning for Fixed-Wing Aircraft in Loss of Thrust Emergencies
Venue: arXiv cs.SY 2017
Year: 2017

Loss of thrust emergencies-e.g., induced by bird/drone strikes or fuel exhaustion-create the need for dynamic data-driven flight trajectory planning to advise pilots or control UAVs. While total loss of thrust trajectories to nearby airports can be pre-computed for all initial points in a 3D flight plan, dynamic aspects such as partial power and airplane surface damage must be considered for accuracy. In this paper, we propose a new Dynamic Data-Driven Avionics Software (DDDAS) approach which during flight updates a damaged aircraft performance model, used in turn to generate plausible flight trajectories to a safe landing site. Our damaged aircraft model is parameterized on a baseline glide ratio for a clean aircraft configuration assuming best gliding airspeed on straight flight. The model predicts purely geometric criteria for flight trajectory generation, namely, glide ratio and turn radius for different bank angles and drag configurations. Given actual aircraft performance data, we dynamically infer the baseline glide ratio to update the damaged aircraft model. Our new flight trajectory generation algorithm thus can significantly improve upon prior Dubins based trajectory generation work by considering these data-driven geometric criteria. We further introduce a trajectory utility function to rank trajectories for safety. As a use case, we consider the Hudson River ditching of US Airways 1549 in January 2009 using a flight simulator to evaluate our trajectories and to get sensor data. In this case, a baseline glide ratio of 17.25:1 enabled us to generate trajectories up to 28 seconds after the birds strike, whereas, a 19:1 baseline glide ratio enabled us to generate trajectories up to 36 seconds after the birds strike. DDDAS can significantly improve the accuracy of generated flight trajectories thereby enabling better decision support systems for pilots in emergency conditions.
Knee Motion Generation Method for Transfemoral Prosthesis based on Kinematic Synergy and Inertial Motion
Venue: arXiv cs.RO 2017
Year: 2017

Previous research has shown that the effective use of inertial motion (i.e., less or no torque input at the knee joint) plays an important role in achieving a smooth gait of transfemoral prostheses in the swing phase. In our previous research, a method for generating a timed knee trajectory close to able-bodied individuals, which leads to sufficient clearance between the foot and the floor and the knee extension, was proposed using the inertial motion. Limb motions are known to correlate with each other during walking. This phenomenon is called kinematic synergy. In the present study, we measure gaits in level walking of able-bodied individuals with a wide range of walking velocities. We show that this kinematic synergy also exists between the motions of the intact limbs and those of the knee as determined by the inertial motion technique. We then propose a new method for generating the motion of the knee joint using its inertial motion close to the able-bodied individuals in mid-swing based on its kinematic synergy, such that the method can adapt to the changes in the motion velocity. The numerical simulation results show that the proposed method achieves prosthetic walking similar to that of able-bodied individuals with a wide range of constant walking velocities and termination of walking from steady-state walking. Further investigations have found that a kinematic synergy also exists at the start of walking. Overall, our method successfully achieves knee motion generation from the initiation of walking through steady-state walking with different velocities until termination of walking.
Talos: Neutralizing Vulnerabilities with Security Workarounds for Rapid Response
Venue: arXiv cs.CR 2017
Year: 2017

Considerable delays often exist between the discovery of a vulnerability and the issue of a patch. One way to mitigate this window of vulnerability is to use a configuration workaround, which prevents the vulnerable code from being executed at the cost of some lost functionality -- but only if one is available. Since program configurations are not specifically designed to mitigate software vulnerabilities, we find that they only cover 25.2% of vulnerabilities. To minimize patch delay vulnerabilities and address the limitations of configuration workarounds, we propose Security Workarounds for Rapid Response (SWRRs), which are designed to neutralize security vulnerabilities in a timely, secure, and unobtrusive manner. Similar to configuration workarounds, SWRRs neutralize vulnerabilities by preventing vulnerable code from being executed at the cost of some lost functionality. However, the key difference is that SWRRs use existing error-handling code within programs, which enables them to be mechanically inserted with minimal knowledge of the program and minimal developer effort. This allows SWRRs to achieve high coverage while still being fast and easy to deploy. We have designed and implemented Talos, a system that mechanically instruments SWRRs into a given program, and evaluate it on five popular Linux server programs. We run exploits against 11 real-world software vulnerabilities and show that SWRRs neutralize the vulnerabilities in all cases. Quantitative measurements on 320 SWRRs indicate that SWRRs instrumented by Talos can neutralize 75.1% of all potential vulnerabilities and incur a loss of functionality similar to configuration workarounds in 71.3% of those cases. Our overall conclusion is that automatically generated SWRRs can safely mitigate 2.1x more vulnerabilities, while only incurring a loss of functionality comparable to that of traditional configuration workarounds.
Channel masking for multivariate time series shapelets
Venue: arXiv cs.LG 2017
Year: 2017

Time series shapelets are discriminative sub-sequences and their similarity to time series can be used for time series classification. Initial shapelet extraction algorithms searched shapelets by complete enumeration of all possible data sub-sequences. Research on shapelets for univariate time series proposed a mechanism called shapelet learning which parameterizes the shapelets and learns them jointly with a prediction model in an optimization procedure. Trivial extension of this method to multivariate time series does not yield very good results due to the presence of noisy channels which lead to overfitting. In this paper we propose a shapelet learning scheme for multivariate time series in which we introduce channel masks to discount noisy channels and serve as an implicit regularization.
Towards Neural Machine Translation with Partially Aligned Corpora
Venue: arXiv cs.CL 2017
Year: 2017

While neural machine translation (NMT) has become the new paradigm, the parameter optimization requires large-scale parallel data which is scarce in many domains and language pairs. In this paper, we address a new translation scenario in which there only exists monolingual corpora and phrase pairs. We propose a new method towards translation with partially aligned sentence pairs which are derived from the phrase pairs and monolingual corpora. To make full use of the partially aligned corpora, we adapt the conventional NMT training method in two aspects. On one hand, different generation strategies are designed for aligned and unaligned target words. On the other hand, a different objective function is designed to model the partially aligned parts. The experiments demonstrate that our method can achieve a relatively good result in such a translation scenario, and tiny bitexts can boost translation quality to a large extent.
Dynamic FPGA Detection and Protection of Hardware Trojan: A Comparative Analysis
Venue: arXiv cs.CR 2017
Year: 2017

Hardware Trojan detection and protection is becoming more crucial as more untrusted third parties manufacture many parts of critical systems nowadays. The most common way to detect hardware Trojans is comparing the untrusted design with a golden (trusted) one. However, third-party intellectual properties (IPs) are black boxes with no golden IPs to trust. So, previous attempts to detect hardware Trojans will not work with third-party IPs. In this work, we present novel methods for Trojan protection and detection on field programmable gate arrays (FPGAs) without the need for golden chips. Presented methods work at runtime instead of test time. We provide a wide spectrum of Trojan detection and protection methods. While the simplest methods have low overhead and provide limited protection mechanisms, more sophisticated and costly techniques are introduced that can detect hardware Trojans and even clean up the system from infected IPs. Moreover, we study the cost of using the FPGA partial reconfiguration feature to get rid of infected IPs. In addition, we discuss the possibility to construct IP core certificate authority that maintains a centralized database of unsafe vendors and IPs. We show the practicality of the introduced schemes by implementing the different methodologies on FPGAs. Results show that simple methods present negligible overheads and as we try to increase security the delay and power overheads increase.
A Searchable Symmetric Encryption Scheme using BlockChain
Venue: arXiv cs.CR 2017
Year: 2017

At present, the cloud storage used in searchable symmetric encryption schemes (SSE) is provided in a private way, which cannot be seen as a true cloud. Moreover, the cloud server is thought to be credible, because it always returns the search result to the user, even they are not correct. In order to really resist this malicious adversary and accelerate the usage of the data, it is necessary to store the data on a public chain, which can be seen as a decentralized system. As the increasing amount of the data, the search problem becomes more and more intractable, because there does not exist any effective solution at present. In this paper, we begin by pointing out the importance of storing the data in a public chain. We then innovatively construct a model of SSE using blockchain(SSE-using-BC) and give its security definition to ensure the privacy of the data and improve the search efficiency. According to the size of data, we consider two different cases and propose two corresponding schemes. Lastly, the security and performance analyses show that our scheme is feasible and secure.